Comparative Study of KNN, SVM and Decision Tree Algorithm for Student’s Performance Prediction

Slamet Wiyono, Dega Surono Wibowo, M. Fikri Hidayatullah, Dairoh Dairoh

Abstract


Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to obtain the best predictive model. The model making process was carried out by the following steps: data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN algorithm has a prediction accuracy value of 92%.

Keywords


KNN; SVM; decision tree

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References


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DOI: http://dx.doi.org/10.12962/j24775401.v6i2.4360

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International Journal of Computing Science and Applied Mathematics by Pusat Publikasi Ilmiah LPPM, Institut Teknologi Sepuluh Nopember is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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